Optimizing explicit feature maps on intervals

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00315488" target="_blank" >RIV/68407700:21230/17:00315488 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.imavis.2017.07.001" target="_blank" >http://dx.doi.org/10.1016/j.imavis.2017.07.001</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.imavis.2017.07.001" target="_blank" >10.1016/j.imavis.2017.07.001</a>

Result language
angličtina
Original language name
Optimizing explicit feature maps on intervals
Original language description
Approximating non-linear kernels by finite-dimensional feature maps is a popular approach for accelerating training and evaluation of support vector machines or to encode information into efficient match kernels. We propose a novel method of data independent construction of low-dimensional feature maps. The problem is formulated as a linear program that jointly considers two competing objectives: the quality of the approximation and the dimensionality of the feature map.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/LL1303" target="_blank" >LL1303: Large Scale Category Retrieval</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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